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This book provides statisticians with an understanding of the critical challenges currently encountered in oncology trials. The book covers state-of-the-art approaches to the design and analysis of cancer clinical trials, such as adaptive designs, biomarker-based trials, and dynamic treatment regimes.

This resource provides a thorough presentation of the design, monitoring, analysis, and interpretation of clinical trials in which time-to-event is of critical interest. Incorporating the collaborations of truly world-class statisticians, it discusses the design and monitoring of Phase II and III clinical trials with time-to-event endpoints. The book presents parametric, semiparametric, categorical, and Bayesian inferential and descriptive methods for analyzing time-to-event endpoints and covers numerous clinical trial applications, including analgesic, antibiotic, antiviral, cancer and cardiovascular prevention, optimal assignment of treatments, adverse events, and carcinogenicity.

Helping you become a skillful "simulator," this book provides broad coverage of the drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and methods for carrying out computer simulations efficiently, covers both descriptive and pseudocode algorithms that provide the basis for implementing the simulation methods, and illustrates real-world problems through case studies. The author discusses many key topics, including game theory, adaptive design, molecular design, prescription drug marketing, biological pathway simulation, genetic programming, and pharmacokinetic modeling.

Showcasing a discussion of the experimental process and a review of basic statistics, this volume provides methodologies to identify general data distribution, skewness, and outliers. It features a unique classification of the nonparametric analogs of their parametric counterparts according to the strength of the collected data. Applied Statistical Designs for the Researcher discusses three varieties of the Student t test, including a comparison of two different groups with different variances; two groups with the same variance; and a matched, paired group. It introduces the analysis of variance and Latin Square designs and presents screening approaches to comparing two factors and their interactions.

This volume discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research. It presents a broad overview of statistical inference, clustering, and classification problems in two main high-throughput platforms: microarray gene expression and phylogenic analysis. Illustrating concepts using real-world data, the book covers a variety of recently developed Bayesian techniques, along with applications in genome-wide studies, phylogenetics, breast cancer, expression genomics, and more.

Methodologies in Biosimilar Product Development covers the practical and challenging issues that are commonly encountered during the development, review, and approval of a proposed biosimilar product.

This book concentrates on the biostatistics component of clinical trials. This new, second edition is updated throughout and includes three new chapters. This text reflects the academic research, commercial development, and public health aspects of clinical trials.

Employs a Bayesian approach to provide statistical inferences based on various models of intra- and inter rater agreement. This book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation. It discusses how to successfully design and analyze an agreement study.

This book contains chapters providing interpretations of principles in ICH E17 and new ideas of implementing MRCTs. Authors are from different regions, and from academia and industry. This book will be of particular interest to biostatisticians working in late stage clinical development of medical products.

This book is focused on the critical clinical initiatives introduced by the 21st Century Cure Act passed by the United States Congress in December 2016. The book covers everything from the outline of the initiatives to analysis on the effect on biopharmaceutical research and development.

This book provides a comprehensive coverage on safety monitoring methodologies, covering both global trends and regional initiatives. Pharmacovigilance has traditionally focused on the handling of individual adverse event reports however recently there had been a shift towards aggregate analysis to better understand the scope of product risks.

It is critical for non-clinical statisticians to communicate effectively with scientists. Therefore, they must possess the ability to transform scientific questions into statistical hypotheses and models. This book affords new and experienced statisticians with the opportunity to enhance their statistical tools and improve their consulting skills.

Intended to be a single source of information, this book covers a wide range of topics on the changing landscape of drug R&D, emerging applications of big data, AI and machine learning in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations.

Methodologies for Biosimilar Product Development covers the practical and challenging issues that are commonly encountered during the development, review, and approval of a proposed biosimilar product. These practical and challenging issues include, but are not limited to the mix-up use of interval hypotheses testing (i.e., the use of TOST) and confidence interval approach, a risk/benefit assessment for non-inferiority/similarity margin, PK/PD bridging studies with multiple references, the detection of possible reference product change over time, design and analysis of biosimilar switching studies, the assessment of sensitivity index for assessment of extrapolation across indications without collecting data from those indications not under study, and the feasibility and validation of non-medical switch post-approval.Key Features:Reviews withdrawn draft guidance on analytical similarity assessment.Evaluates various methods for analytical similarity evaluation based on FDA's current guidelines.Provides a general approach for the use of n-of-1 trial design for assessment of interchangeability.Discusses the feasibility and validity of the non-medical switch studies.Provides innovative thinking for detection of possible reference product change over time.This book embraces innovative thinking of design and analysis for biosimilar studies, which are required for review and approval of biosimilar regulatory submissions.

This book shows how model-assisted designs can greatly improve the efficiency and simplify the conduct of early-phase dose finding and optimization trials. It should therefore be a very useful practical reference for biostatisticians, clinicians working in clinical trials, and drug regulatory professionals, as well as grad students.

The subject of this book is applied Bayesian methods for chemistry, manufacturing, and control (CMC) studies in the biopharmaceutical industry. The book has multiple authors from industry and academia, each contributing a case study (chapter), covering a broad array of CMC topics.

A disease is defined as rare if the prevalence is fewer than 200,000 in the United States. It is estimated that there are more than 7,000 rare diseases, which collectively affect 30 million Americans. This diverse and complex disease area poses challenges for patients, caregivers, regulators, drug developers and other stakeholders.